1. Field
The disclosed concept pertains generally to electric loads and, more particularly, to methods of identifying electric load types of electric loads. The disclosed concept also pertains to systems for identifying electric load types of electric loads.
2. Background Information
Electricity usage costs have become an increasing fraction of the total cost of ownership for commercial buildings. At the same time, miscellaneous electric loads (MELs) account for about 36% of electricity consumption of commercial buildings. Effective management of MELs could potentially improve energy savings of buildings up to about 10%. However, power consumption monitoring and energy management of MELs inside commercial buildings is often overlooked. In order to provide the MELs' energy consumption conditions by load type to a building automation system (BAS), and, consequently, to manage the MELs and reduce energy consumption inside commercial buildings, there is a need to identify the MELs.
Lam, H. Y. et al., “A novel method to construct taxonomy of electrical appliances based on load signatures,” IEEE Transactions on Consumer Electronics, vol. 53, no. 2, 2007, p. 653-60, discloses that a load signature is an electrical expression that a load device or appliance distinctly possesses. Load signatures can be applied to produce many useful services and products, such as, determining the energy usage of individual appliances, monitoring the health of critical equipment, monitoring power quality, and developing facility management tools. Load signatures of typical yet extensive loads are needed to be collected before applying them to different services and products. As there are an enormous number of electrical appliances, it is beneficial to classify the appliances for building a well-organized load signature database. A method to classify the loads employs a two-dimensional form of load signatures, voltage-current (V-I) trajectory, for characterizing typical household appliances. A hierarchical clustering method uses a hierarchical decision tree or dendrogram to show how objects are related to each other. Groups of the objects can be determined from the dendrogram, to classify appliances and construct the taxonomy of the appliances. The taxonomy based on V-I trajectory is compared to the taxonomies based on traditional power metrics and eigenvectors in prior studies.
In this taxonomy approach, only one set of load features is utilized, and the hierarchical structure of appliances, a dendrogram, is based on the selection of a distance value/threshold between the groups in each level, or the height of a cluster tree. In this approach, the selection of the distance/height will affect how the hierarchical tree is built.
The power usage monitoring of MELs by types in residential, commercial or industrial buildings provides an opportunity to effectively manage MELs and potentially improve energy savings of buildings. This needs an accurate and un-ambiguous identification of MELs that are plugged into, for example, power outlets.
To successfully identify MELs, the biggest challenge is to distinguish the loads with the most similarity, for example and without limitation, a DVD, a set top box, and a PC monitor (e.g., those using a standardized DC power supply, and current harmonic reduction techniques). This difficulty has not been explicitly addressed and solved by known techniques.
Today, a majority of MELs connected to a building remain unidentified due to the lack of intelligence of building management systems or BASs. The operational status and energy consumption of loads needs to be communicated to a building management system or BAS in an automatic, low cost and non-intrusive manner. The electric loads often present unique characteristics in their electric signals (e.g., voltage, current and power). Such load characteristics provide a viable means to identify the type of the load (e.g., without limitation, PC; heater; lamp) and its operational status (e.g., without limitation, active; ready; standby) by analyzing the unique characteristics of the corresponding electric signals (e.g., voltage, current and power of plugged loads at a power outlet or intelligent receptacle).
Known proposals for detecting single-phase electric loads are based on voltage, current and/or power measurements, including, relative position in active-reactive power plane (P-Q plane); variation in active and reactive power at different operating conditions; harmonic power contents and harmonic currents; steady-state two-dimensional voltage-current (V-I) trajectories; instantaneous power; instantaneous admittance; and power factor.
Prior proposals employ a variation in active and reactive power at different operating conditions as an indication of the characteristics of the load, obtained from continuous measurement of current and voltage. Subsequent identification is then made by comparing active and reactive power values with a library of known characteristics of typical loads. These methods are extended by including harmonic power contents and harmonic currents as additional features.
A recent prior proposal considers the use of steady-state two-dimensional voltage-current trajectories to identify and classify electric loads. The measured voltage and current time series in one power line cycle are plotted on a V-I plane 100 as shown in FIG. 7. The different features of the shape are used to characterize different loads, such as resistive, inductive, and rectifier types. The shape signature includes features, such as asymmetry, area, curvature of mean line 101, slope of middle segment, area of left and right segment, and peak height of middle segment. These signatures are used to cluster and identify loads.
Another prior proposal uses transient signatures for load classification and identification.
However, it is believed that these proposals suffer from several serious disadvantages in their accuracy, robustness and applicability, including: (1) MELs with different voltage and current characteristics may be grouped together by the identifier if they consume approximately the same amount of active and reactive power; (2) MELs of the same type may be grouped separately by the identifier if they have different power ratings; (3) steady-state operation is usually required for load detection, while many building loads are dynamic in nature; and (4) MELs with similar current features cannot be distinguished, such as DVD players and set top boxes (e.g., MELs with same type of DC power supply). These disadvantages together with the lack of an intelligent power outlet/strip capable of acquiring signals and processing algorithms have impeded the applications of these methods in the real world.
There is room for improvement in methods of identifying electric load types of electric loads.
There is further room for improvement in systems for identifying electric load types of electric loads.